Instructions to use dessertlab/offensive-powershell-codegen-350M-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dessertlab/offensive-powershell-codegen-350M-multi with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dessertlab/offensive-powershell-codegen-350M-multi")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dessertlab/offensive-powershell-codegen-350M-multi")
model = AutoModelForCausalLM.from_pretrained("dessertlab/offensive-powershell-codegen-350M-multi")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dessertlab/offensive-powershell-codegen-350M-multi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dessertlab/offensive-powershell-codegen-350M-multi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dessertlab/offensive-powershell-codegen-350M-multi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dessertlab/offensive-powershell-codegen-350M-multi

SGLang

How to use dessertlab/offensive-powershell-codegen-350M-multi with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dessertlab/offensive-powershell-codegen-350M-multi" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dessertlab/offensive-powershell-codegen-350M-multi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dessertlab/offensive-powershell-codegen-350M-multi" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dessertlab/offensive-powershell-codegen-350M-multi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dessertlab/offensive-powershell-codegen-350M-multi with Docker Model Runner:
```
docker model run hf.co/dessertlab/offensive-powershell-codegen-350M-multi
```

LucianPi commited on May 2, 2024

Commit

3d51fd2

verified ·

1 Parent(s): 52ec573

Delete config.json

Browse files

Files changed (1) hide show

config.json +0 -43

config.json DELETED Viewed

@@ -1,43 +0,0 @@
-{
-  "_name_or_path": "cridin1/codegen-350M-multi-30-1-powershell-last",
-  "activation_function": "gelu_new",
-  "architectures": [
-    "CodeGenModel"
-  ],
-  "attn_pdrop": 0.0,
-  "bos_token_id": 50295,
-  "embd_pdrop": 0.0,
-  "eos_token_id": 50296,
-  "gradient_checkpointing": false,
-  "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "model_type": "codegen",
-  "n_ctx": 2048,
-  "n_embd": 1024,
-  "n_head": 16,
-  "n_inner": null,
-  "n_layer": 20,
-  "n_positions": 2048,
-  "pad_token_id": 50297,
-  "resid_pdrop": 0.0,
-  "rotary_dim": 32,
-  "scale_attn_weights": true,
-  "summary_activation": null,
-  "summary_first_dropout": 0.1,
-  "summary_proj_to_labels": true,
-  "summary_type": "cls_index",
-  "summary_use_proj": true,
-  "task_specific_params": {
-    "text-generation": {
-      "do_sample": true,
-      "max_length": 50,
-      "temperature": 1.0
-    }
-  },
-  "tie_word_embeddings": false,
-  "tokenizer_class": "GPT2Tokenizer",
-  "torch_dtype": "float32",
-  "transformers_version": "4.33.2",
-  "use_cache": true,
-  "vocab_size": 50298
-}